Embodiments of the present application relate generally to recognizing a material within a volume of interest using x-ray. Particularly, certain embodiments of the present application relate to recognizing the presence of materials, such as metallic implants or metallic tools that may impact a quality of an x-ray image if such materials are unaccounted for.
Generally speaking, an x-ray imaging system generates image data by exposing a volume of interest to x-rays, and then detecting x-rays with a detector after they have passed through the volume of interest. Some of the x-ray energy is absorbed or attenuated while passing through the volume of interest. X-ray attenuation is the decrease in the number of photons in an x-ray beam due to interactions with the elements (atoms) of a material substance. The amount of x-ray attenuation depends on the elemental composition of the volume of interest. Different elements have different x-ray attenuation properties. As the x-rays travel through a volume of interest, such as a chest cavity, portions of the x-ray beam are attenuated by differing amounts. Thus, if an x-ray beam was substantially uniform before passing through the volume of interest, it becomes non-uniform after passing through the volume of interest. The resulting non-uniform shadow of x-ray energy may be detected by the detector as x-ray image data. For example, bone is generally a better attenuator of x-rays than soft tissue and air. This is because calcium (an element commonly found in bone) is a better attenuator of x-rays than nitrogen, carbon, hydrogen, and oxygen (elements commonly found in soft tissue and air). Thus, bone may appear darker than surrounding soft-tissue in an x-ray image.
At some time during or subsequent to detection, x-ray image data may be converted into a digital format. The digitized x-ray image data may need further processing before a clinician views or diagnoses the images. Image processing, such as gray scale processing, may be used on the digitized x-ray image data to improve the appearance and clinical usefulness of the x-ray image data. Certain types of post-detection processing, such as gray scale mapping and histogram manipulation, are known to be helpful for automatically adjusting x-ray image data. Post-detection processing may generate image data that is more helpful to a clinician than unprocessed x-ray image data.
One method of post-detection processing involves adjusting the brightness and contrast of x-ray image data. For example, a clinician may wish to view an x-ray image of a chest cavity to view the boney vertebral column in the media steinum region of a patient. The clinician may not be as interested in the nearby soft tissue, such as the lungs. Because the vertebrae are relatively good attenuators of x-rays, these bones may appear darker than the surrounding soft-tissue. Resulting images may be too dark for the clinician to resolve finer detail in the bones. Therefore, a post-detection processing algorithm may brighten the entire image. By doing this, the bone will become brighter, and finer detail may become more apparent to the clinician. At the same time, the brighter soft tissue areas may become washed out—i.e. detail becomes lost in the brightened image because brightening causes saturation. This may be an acceptable tradeoff, nonetheless, because in this example, the clinician is primarily interested in the detail of bone, and not surrounding soft tissue. Thus, post-detection processing may involve a tradeoff between optimizing darker areas versus optimizing lighter areas.
For certain radiological applications, post-detection processing may be configured to automatically react to the presence of darker areas. For example, post-detection processing may be configured to automatically brighten x-ray image data if a darker area is detected. This may be advantageous if, for example, the darker area is presumed to be bone, and the clinician is making a diagnosis based on the appearance of the bone. Conversely, post-detection processing may be configured to automatically darken x-ray image data if a lighter area is detected.
The presence of certain foreign objects in a volume of interest, such as an orthopedic implant, may interfere with the intended operation of automatic post-detection processing. In particular, foreign objects that are relatively good attenuators of x-ray energy may interfere with automatic post-detection processing. Automatic post-detection processing may attempt to brighten an entire image to expose detail in a dark area. However, in the case of metal orthopedic implants, for example, a clinician may not wish to see a brightened gray scale for the metal. Moreover, automatic brightening that is sufficient to lighten the very dark metal image may result in excessive brightening of anatomy, thus washing out soft tissue and/or bone. Thus, the automatic post-detection processing may frustrate clinical use of an x-ray image.
Various schemes attempt to correct this gray scale adjustment problem. For example, algorithms that detect the shape of a metal tool or implant may detect the presence of a foreign object in an image. However, such foreign objects may be any of a variety of shapes and sizes. Additionally, patient anatomies and x-ray imaging angles also exhibit a diversity of geometry. Therefore, it may be difficult to provide a cost-effective object detection routine that reliably detects a foreign object within a patient anatomy.
As another example, regionally adaptive image processing routines may adapt or process portions or regions of an image. Regionally adaptive processing routines may, however, still degrade portions of an image where patient anatomy is proximate to a foreign object.
Thus, there is a need for methods and systems that estimate the presence of a foreign object in x-ray image data of a patient. There is a need for methods and systems that compensate automatic post-detection processing in response to an identified presence of a foreign object. Additionally, there is a need for methods and systems that enhance the clinical usefulness of an x-ray image including both anatomy and a foreign object. Moreover, there is a need for methods and systems that adapt subsequent x-ray source generation based on the presence of a foreign object.